1. DATA EDA

In [28]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.metrics import mean_squared_error

1.1 Variables 1.1.1 Numeric Variable 1.1.2 Categoric Variable(including date variables)

1.2 Target Variable

In [3]:
#bring data
df = pd.read_csv("C:/Users/KIM/Desktop/자기개발/kaggle competition/house-prices-advanced-regression-techniques/train.csv", index_col = 0)
In [4]:
df
Out[4]:
MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub Inside ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub FR2 ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub Inside ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub Corner ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub FR2 ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1456 60 RL 62.0 7917 Pave NaN Reg Lvl AllPub Inside ... 0 NaN NaN NaN 0 8 2007 WD Normal 175000
1457 20 RL 85.0 13175 Pave NaN Reg Lvl AllPub Inside ... 0 NaN MnPrv NaN 0 2 2010 WD Normal 210000
1458 70 RL 66.0 9042 Pave NaN Reg Lvl AllPub Inside ... 0 NaN GdPrv Shed 2500 5 2010 WD Normal 266500
1459 20 RL 68.0 9717 Pave NaN Reg Lvl AllPub Inside ... 0 NaN NaN NaN 0 4 2010 WD Normal 142125
1460 20 RL 75.0 9937 Pave NaN Reg Lvl AllPub Inside ... 0 NaN NaN NaN 0 6 2008 WD Normal 147500

1460 rows × 80 columns

In [5]:
#1.1 Variables
#1.1.1 Numeric Variables
"""
LotFrontage, LotArea, MasVnrArea, BsmtFinSF1, BsmtFinSF2, BsmtUnfSF, (6)
TotalBsmtSF, 1stFlrSF, 2ndFlrSF, LowQualFinSF, GrLivArea, BsmtFullBath,(6)
BsmtHalfBath, FullBath, HalfBath, BedroomAbvGr, KitchenAbvGr, TotRmsAbvGrd, (6)
Fireplaces, GarageYrBlt, GarageCars, GarageArea, WoodDeckSF, (5)
OpenPorchSF,EnclosedPorch,3SsnPorch,ScreenPorch,PoolArea, (5)
MiscVal(1)
"""
numerical = [
'LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',
'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',
'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 
'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 
'OpenPorchSF','EnclosedPorch','3SsnPorch','ScreenPorch','PoolArea', 'MiscVal'  
]
df[numerical].hist(bins=15, figsize=(30, 20), layout=(6, 5))
Out[5]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000222AAC7CB08>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222AACB2488>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222AB975088>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222AB9AE188>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222AB9E5248>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABA23E88>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABA58488>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABA8E588>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABA99488>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABAD2688>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABB38888>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABB6D988>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABBA6AC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABBDFBC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABC16C88>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABC4FDC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABC86EC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABCBFFC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABCFB148>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABD331C8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABD6D2C8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABDA6408>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABDDE508>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABE16608>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABE4D6C8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABE85788>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABEBD888>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABEF59C8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABF2DAC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x00000222ABF66BC8>]],
      dtype=object)
In [6]:
#column that only has "0"

zero_column = df[numerical].sum()
zero_column
Out[6]:
LotFrontage         84130.0
LotArea          15354569.0
MasVnrArea         150551.0
BsmtFinSF1         647714.0
BsmtFinSF2          67962.0
BsmtUnfSF          828171.0
TotalBsmtSF       1543847.0
1stFlrSF          1697435.0
2ndFlrSF           506609.0
LowQualFinSF         8533.0
GrLivArea         2212577.0
BsmtFullBath          621.0
BsmtHalfBath           84.0
FullBath             2285.0
HalfBath              559.0
BedroomAbvGr         4185.0
KitchenAbvGr         1528.0
TotRmsAbvGrd         9516.0
Fireplaces            895.0
GarageYrBlt       2728360.0
GarageCars           2580.0
GarageArea         690551.0
WoodDeckSF         137597.0
OpenPorchSF         68124.0
EnclosedPorch       32053.0
3SsnPorch            4978.0
ScreenPorch         21989.0
PoolArea             4028.0
MiscVal             63494.0
dtype: float64
In [7]:
#1.1.2 Categorical Variables 
categorical1 = ['MSSubClass','MSZoning','Street','Alley','LotShape','LandContour','Utilities']
categorical2 = ['LotConfig','LandSlope','Neighborhood','Condition1','Condition2','BldgType','HouseStyle']
categorical3 = ['OverallQual','OverallCond','YearBuilt','YearRemodAdd','RoofStyle','RoofMatl','Exterior1st']
categorical4 = ['Exterior2nd','MasVnrType','ExterQual','ExterCond','Foundation','BsmtQual','BsmtCond']
categorical5 = ['BsmtExposure','BsmtFinType1','BsmtFinType2','Heating','HeatingQC','CentralAir','Electrical']
categorical6 = ['KitchenQual','Functional','FireplaceQu','GarageType','GarageFinish','GarageQual','GarageCond']
categorical7 = ['PavedDrive','PoolQC','Fence','MiscFeature','MoSold','YrSold','SaleType','SaleCondition']

fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical1, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)

fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical2, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)
    
fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical3, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)
    
fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical4, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)
    
fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical5, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)

fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical6, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)
    
fig, ax = plt.subplots(4,2 , figsize=(15, 10))
for var, subplot in zip(categorical7, ax.flatten()):
    sns.boxplot(x=var, y='SalePrice', data=df, ax=subplot)
In [9]:
#1.2 target variable : SalePrice
df['SalePrice'].hist(bins=15, figsize=(8, 5))
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x222b24c7708>

2. Data Preprocessing

2.1 Fill nans

In [10]:
#fill nans with something else

#1.0 count nans
print("total nan count: ", df.isna().sum().sum())
print("nans in columns: ", df.isna().sum())

#1.0.0 if a variable has too many nans : delete that variable(column)
#1.0.1 fill nans with avg of each columns : numerical variables
df_numerical = df[numerical].fillna(df[numerical].mean())

#1.0.2 fill nans : categorical??  : fill with most frequent value??
categorical = categorical1 + categorical2 + categorical3 + categorical4 + categorical5 + categorical6 + categorical7 
df_categorical = df[categorical].apply(lambda x: x.fillna(x.value_counts().index[0]))

#1.0.3 y variale
y = df['SalePrice']

df=pd.concat([df_numerical, df_categorical,y], axis = 1)

print("total nan count: ", df.isna().sum().sum())
print("nans in columns: ",df.isna().sum())

total nan count:  6965
nans in columns:  MSSubClass         0
MSZoning           0
LotFrontage      259
LotArea            0
Street             0
                ... 
MoSold             0
YrSold             0
SaleType           0
SaleCondition      0
SalePrice          0
Length: 80, dtype: int64
total nan count:  0
nans in columns:  LotFrontage      0
LotArea          0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
                ..
MoSold           0
YrSold           0
SaleType         0
SaleCondition    0
SalePrice        0
Length: 80, dtype: int64
In [11]:
df
Out[11]:
LotFrontage LotArea MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF ... GarageCond PavedDrive PoolQC Fence MiscFeature MoSold YrSold SaleType SaleCondition SalePrice
Id
1 65.0 8450 196.0 706 0 150 856 856 854 0 ... TA Y Gd MnPrv Shed 2 2008 WD Normal 208500
2 80.0 9600 0.0 978 0 284 1262 1262 0 0 ... TA Y Gd MnPrv Shed 5 2007 WD Normal 181500
3 68.0 11250 162.0 486 0 434 920 920 866 0 ... TA Y Gd MnPrv Shed 9 2008 WD Normal 223500
4 60.0 9550 0.0 216 0 540 756 961 756 0 ... TA Y Gd MnPrv Shed 2 2006 WD Abnorml 140000
5 84.0 14260 350.0 655 0 490 1145 1145 1053 0 ... TA Y Gd MnPrv Shed 12 2008 WD Normal 250000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1456 62.0 7917 0.0 0 0 953 953 953 694 0 ... TA Y Gd MnPrv Shed 8 2007 WD Normal 175000
1457 85.0 13175 119.0 790 163 589 1542 2073 0 0 ... TA Y Gd MnPrv Shed 2 2010 WD Normal 210000
1458 66.0 9042 0.0 275 0 877 1152 1188 1152 0 ... TA Y Gd GdPrv Shed 5 2010 WD Normal 266500
1459 68.0 9717 0.0 49 1029 0 1078 1078 0 0 ... TA Y Gd MnPrv Shed 4 2010 WD Normal 142125
1460 75.0 9937 0.0 830 290 136 1256 1256 0 0 ... TA Y Gd MnPrv Shed 6 2008 WD Normal 147500

1460 rows × 80 columns

2.2 Drop numerical variables with multicollinearity

In [12]:
#2.1 multicollinearity checking

#2.1.1 check multicollinearity among "numerical variables": If VIF >10 then that variable has multicollinearity
vif_data = pd.DataFrame()
vif_data["feature"] = df[numerical].columns
vif_data["VIF"] = [variance_inflation_factor(df[numerical].values, i) for i in range(len(df[numerical].columns))]

C:\Users\KIM\anaconda3\lib\site-packages\statsmodels\stats\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars
  vif = 1. / (1. - r_squared_i)
In [13]:
vif_data
Out[13]:
feature VIF
0 LotFrontage 15.617399
1 LotArea 2.587356
2 MasVnrArea 1.799190
3 BsmtFinSF1 inf
4 BsmtFinSF2 inf
5 BsmtUnfSF inf
6 TotalBsmtSF inf
7 1stFlrSF inf
8 2ndFlrSF inf
9 LowQualFinSF inf
10 GrLivArea inf
11 BsmtFullBath 3.564222
12 BsmtHalfBath 1.197847
13 FullBath 19.458272
14 HalfBath 2.997632
15 BedroomAbvGr 28.416082
16 KitchenAbvGr 29.411489
17 TotRmsAbvGrd 81.424035
18 Fireplaces 2.888910
19 GarageYrBlt 47.722183
20 GarageCars 34.621898
21 GarageArea 30.642974
22 WoodDeckSF 1.877392
23 OpenPorchSF 1.800334
24 EnclosedPorch 1.272064
25 3SsnPorch 1.033408
26 ScreenPorch 1.169930
27 PoolArea 1.096759
28 MiscVal 1.024630
In [14]:
#IF VIF >10 then that variable is known to have multicollinearity with other variables
df.drop(['BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','1stFlrSF','2ndFlrSF','LowQualFinSF','GrLivArea','TotRmsAbvGrd'], axis=1, inplace=True)
In [15]:
df
Out[15]:
LotFrontage LotArea MasVnrArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr Fireplaces ... GarageCond PavedDrive PoolQC Fence MiscFeature MoSold YrSold SaleType SaleCondition SalePrice
Id
1 65.0 8450 196.0 1 0 2 1 3 1 0 ... TA Y Gd MnPrv Shed 2 2008 WD Normal 208500
2 80.0 9600 0.0 0 1 2 0 3 1 1 ... TA Y Gd MnPrv Shed 5 2007 WD Normal 181500
3 68.0 11250 162.0 1 0 2 1 3 1 1 ... TA Y Gd MnPrv Shed 9 2008 WD Normal 223500
4 60.0 9550 0.0 1 0 1 0 3 1 1 ... TA Y Gd MnPrv Shed 2 2006 WD Abnorml 140000
5 84.0 14260 350.0 1 0 2 1 4 1 1 ... TA Y Gd MnPrv Shed 12 2008 WD Normal 250000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1456 62.0 7917 0.0 0 0 2 1 3 1 1 ... TA Y Gd MnPrv Shed 8 2007 WD Normal 175000
1457 85.0 13175 119.0 1 0 2 0 3 1 2 ... TA Y Gd MnPrv Shed 2 2010 WD Normal 210000
1458 66.0 9042 0.0 0 0 2 0 4 1 2 ... TA Y Gd GdPrv Shed 5 2010 WD Normal 266500
1459 68.0 9717 0.0 1 0 1 0 2 1 0 ... TA Y Gd MnPrv Shed 4 2010 WD Normal 142125
1460 75.0 9937 0.0 1 0 1 1 3 1 0 ... TA Y Gd MnPrv Shed 6 2008 WD Normal 147500

1460 rows × 71 columns

2.3 Drop categorical variables with multicollinearity, by PCA after one-hot-encoding categorical variables

In [16]:
fin_y = df['SalePrice']
In [17]:
#2.3 One hot encoding only categorcal variables 
fin_x = df.loc[:, df.columns!='SalePrice']
fin_x_one_hot = pd.get_dummies(fin_x)
fin_x_one_hot.head(5)
Out[17]:
LotFrontage LotArea MasVnrArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr Fireplaces ... SaleType_ConLw SaleType_New SaleType_Oth SaleType_WD SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial
Id
1 65.0 8450 196.0 1 0 2 1 3 1 0 ... 0 0 0 1 0 0 0 0 1 0
2 80.0 9600 0.0 0 1 2 0 3 1 1 ... 0 0 0 1 0 0 0 0 1 0
3 68.0 11250 162.0 1 0 2 1 3 1 1 ... 0 0 0 1 0 0 0 0 1 0
4 60.0 9550 0.0 1 0 1 0 3 1 1 ... 0 0 0 1 1 0 0 0 0 0
5 84.0 14260 350.0 1 0 2 1 4 1 1 ... 0 0 0 1 0 0 0 0 1 0

5 rows × 279 columns

In [18]:
#standardization(both numerical and categorical varaibles)
scaler = StandardScaler()
fin_x_stand=scaler.fit_transform(fin_x_one_hot)
print(fin_x_stand.shape)

(1460, 279)
In [19]:
#PCA (both numerical and categorical) #131개로 85% 설명, 151개로 90%설명

# Loop Function to identify number of principal components that explain at least 85% of the variance
for comp in range(3, fin_x_stand.shape[1]):
    pca = PCA(n_components= comp, random_state=42)
    pca.fit(fin_x_stand)
    comp_check = pca.explained_variance_ratio_
    final_comp = comp
    if comp_check.sum() > 0.85:
        break
        
Final_PCA = PCA(n_components= final_comp,random_state=42)
Final_PCA.fit(fin_x_stand)
cluster_df=Final_PCA.transform(fin_x_stand) #final dataframe after PCA
num_comps = comp_check.shape[0]
print("Using {} components, we can explain {}% of the variability in the original data.".format(final_comp,comp_check.sum()))

Using 131 components, we can explain 0.8504905129815726% of the variability in the original data.
In [20]:
cluster_df.shape
Out[20]:
(1460, 131)
In [21]:
#to dataframe
fin_x = pd.DataFrame(cluster_df)

2.3 Remove outliers

In [22]:
#since we don't have a lot of data here, I won't remove any outliers...Maybe for better performances, I will....

2.4 Divide data to train/valid/test data

In [23]:
#concat fin_x and fin_y)
fin_df=pd.concat([fin_x, fin_y], axis = 1)
In [24]:
train_df, validate_df, test_df = np.split(fin_df.sample(frac=1), [int(.4*len(fin_df)), int(.7*len(fin_df))])
In [25]:
train_df
Out[25]:
0 1 2 3 4 5 6 7 8 9 ... 122 123 124 125 126 127 128 129 130 SalePrice
1320 1.848604 -4.066261 -0.027167 1.654556 -1.596239 -0.219456 0.316095 0.023938 0.189747 -0.282273 ... 0.058099 -0.259488 0.667925 0.008952 -0.038146 0.507983 0.352036 -1.507556 0.190584 111000.0
309 -6.464090 0.121125 3.424288 1.383345 -1.453800 0.434944 -0.566364 -0.739324 0.578719 2.022136 ... 0.938911 -0.171392 -0.251452 0.940274 0.785401 0.583321 0.226217 -0.250147 0.639014 82500.0
751 -3.479064 2.253071 -1.256313 -3.318174 -0.780026 -0.837351 1.232485 1.145197 -0.803659 -0.728762 ... -0.330501 -0.465258 0.649816 -1.201147 0.686879 -1.498306 0.273065 0.057768 -0.877124 96500.0
1422 -3.894058 2.471481 -3.184897 0.892515 2.610650 -2.002142 0.255965 -0.704022 2.423512 2.099561 ... 0.322870 -0.372124 0.118197 -0.571605 -0.443687 -0.144770 0.243440 -0.384350 -0.578949 127500.0
867 1.754506 -1.718710 -0.730321 0.262206 -1.531188 -1.149268 2.314404 1.414364 0.096641 0.291313 ... 0.104375 0.634254 0.780303 0.047784 -0.430769 0.070652 0.974944 0.383676 -1.383187 248900.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
704 -4.661512 0.639714 -1.406257 -0.497498 -0.545557 -2.912151 1.445229 0.107580 1.172127 1.046550 ... 0.649289 -0.218130 -0.060713 -0.220868 -1.087713 -1.339558 -0.390723 -0.888748 0.087296 140000.0
510 2.443506 -4.563067 1.402458 1.130627 -0.456752 0.327434 -0.782610 1.628426 0.953320 0.176060 ... 0.831986 1.395367 -2.657604 0.638451 -0.269614 0.585963 2.474561 -0.104986 0.806993 124500.0
1257 3.598513 0.712288 -0.446503 -0.295386 -1.884812 -1.823127 -0.275341 1.100484 0.951111 0.577582 ... -0.685779 1.382168 0.092182 0.232143 -1.553589 -1.035287 -0.837404 0.267741 0.080008 301500.0
532 2.577726 -0.142444 -1.076895 0.336057 -0.731826 -1.941778 1.910293 0.500701 0.494599 -1.358917 ... -1.991046 1.244107 0.665385 0.909369 0.052820 -1.176005 1.387738 1.238806 0.606789 128000.0
676 9.349732 8.465874 6.817668 -2.804081 0.542873 3.668212 4.363106 -6.659886 1.302933 -0.902192 ... -1.100479 1.247859 2.073852 -1.772762 1.438705 1.319859 0.669150 2.588434 -1.186357 148500.0

584 rows × 132 columns

In [26]:
train_df = train_df.dropna(axis=0)
train_x = train_df.drop('SalePrice', axis=1)
train_y = train_df["SalePrice"]

validate_df = validate_df.dropna(axis=0)
valid_x = validate_df.drop('SalePrice', axis=1)
valid_y=validate_df["SalePrice"]

test_df = test_df.dropna(axis=0)
test_x = test_df.drop('SalePrice', axis=1)
test_y = test_df["SalePrice"]

3.1 model1 - random forest

In [38]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import accuracy_score # 정확도 함수

clf = RandomForestRegressor(n_estimators=20, max_depth=5,random_state=0)
clf.fit(train_x,train_y)

predict1 = clf.predict(test_x)
In [28]:
#predict1.shape #(439,)
predict1
Out[28]:
array([170804.15801211, 199305.89852374, 187396.5044018 , 188111.05005443,
       304083.82153772, 166958.33019854, 170482.30885596, 173770.7329315 ,
       153582.7403209 , 228331.33183422, 184249.35380908, 192599.60909106,
       194387.09815769, 164077.01442039, 196425.95753373, 183215.54906983,
       190623.53404909, 168145.2129837 , 164671.43442381, 173001.71390418,
       191359.83521411, 163257.76125947, 319683.80273779, 164875.63403212,
       165236.11471602, 190472.72971538, 190108.08689043, 179788.54681382,
       173064.02231888, 181657.18240534, 170873.5461995 , 172493.15247218,
       218871.41653098, 170846.11678739, 186155.93657377, 181977.47621627,
       181294.39722656, 163264.79041743, 174577.80485473, 180615.9108095 ,
       168186.80761463, 254638.4043283 , 167861.71311936, 196693.3853321 ,
       164102.81785863, 226603.10111305, 181795.28709431, 162481.71098472,
       176740.72222044, 166348.21242801, 194268.65737652, 167875.89419411,
       222501.49909251, 220250.01997822, 189999.12065136, 199951.10583046,
       191732.4656445 , 194851.8515738 , 174477.62856509, 163021.91875119,
       167962.85834697, 173890.30606226, 167014.98879969, 183035.87122158,
       252246.56903918, 166653.76251893, 226763.49642357, 186747.9185184 ,
       198916.08035982, 384420.4515282 , 200146.15895476, 175039.43417718,
       195133.84232917, 188446.20703511, 167335.46702027, 169601.68071932,
       169437.36353673, 176813.05137815, 171906.86905061, 169278.62043299,
       195140.33574884, 160827.60935901, 179824.22332388, 195115.3543186 ,
       201164.01863389, 168365.37293826, 200054.40915732, 181995.51036673,
       178407.11038631, 172026.72154865, 170898.01634362, 173273.78678905,
       213191.53987672, 176260.67781345, 172454.54289675, 192171.28662092,
       167881.22336949, 184489.88766112, 195139.45153702, 339430.18821904,
       205411.76816457, 201178.39466805, 179014.94397595, 166862.78386969,
       182227.0494748 , 255370.17654095, 175142.080869  , 190347.04303251,
       181078.7807537 , 161333.19839201, 175096.94526154, 185404.34855352,
       204158.81520942, 180017.11706599, 186064.70797096, 191849.01337352,
       239212.0819567 , 174167.72995854, 185878.26081111, 197716.57687696,
       179623.2991039 , 167137.00493357, 185875.60095942, 190081.1844764 ,
       177882.7553615 , 170455.81770472, 169857.95486675, 174112.37329201,
       192305.89039775, 165608.06328772, 162753.14095721, 170919.7438847 ,
       325128.40199571, 173454.77772586, 175014.43619662, 156850.95980581,
       165844.87483625, 168525.63797682, 183261.63396273, 172879.00824531,
       180808.63860497, 212513.14104922, 198630.26566338, 198135.59321543,
       165017.95080918, 176814.40063606, 173381.55032265, 174874.42641   ,
       192010.06521203, 159944.09705962, 182999.99472185, 162362.12879271,
       178161.19402537, 169442.4687155 , 194328.777087  , 166068.98955922,
       184824.85077765, 246684.20358169, 168828.98881206, 166069.10802369,
       173100.3203168 , 177910.51376838, 177490.69836571, 377511.46204228,
       166687.07646402, 174105.63164517, 174871.31621484, 176424.40313991,
       165661.28153083, 156063.66172985, 163370.38674013, 167763.47315921,
       210158.76443868, 180423.66459494, 173930.67842892, 169100.59425471,
       177799.41456678, 186390.57785566, 172692.92121524, 263003.42240075,
       180744.06308933, 176950.67934117, 190441.38755677, 176811.84151608,
       186417.49211176, 189982.88892762, 219417.76595006, 185399.45623132,
       163681.54589076, 178367.46054675, 173640.36332863, 183773.58303134,
       208726.9323152 , 178033.94211545, 173640.44550149, 183065.94976253,
       180867.66569955, 166869.94631144, 170162.80861652, 171851.10378418,
       170404.01607785, 157603.80384927, 201945.95567211, 200332.03200545,
       183884.8347831 , 166949.51228959, 192064.24226961, 180216.99528309,
       160789.80090376, 176983.27328179, 181389.05613666, 191843.64808348,
       216541.85292508, 181753.58014209, 176798.52404764, 201856.58004328,
       178167.17473079, 193439.93617814, 162686.05739324, 185112.72893549,
       185920.24940013, 183522.27363016, 185972.84982558, 205748.04058002,
       170442.40999907, 158982.12563842, 170381.93192837, 178069.67566639,
       225801.15737059, 174310.4753669 , 181169.20958472, 181533.41070586,
       213004.32610269, 165010.61710855, 169877.17430719, 165263.38510662,
       172949.10317798, 166627.63977986, 255976.1558128 , 195139.4514161 ,
       162635.71064739, 194529.22086552, 220963.9780075 , 187786.22150152,
       181372.40653233, 203798.46997093, 186325.48379932, 178760.83214566,
       173368.13318118, 206898.01461399, 178265.08710505, 182055.92349157,
       162442.83750575, 258314.02913873, 166256.74245318, 163524.70859323,
       174264.56824742, 169966.13516028, 184056.5050026 , 175677.11213991,
       186657.18391305, 171365.27755388, 280108.8925933 , 195103.56177404,
       184456.91736974, 195922.21465838, 171468.54048636, 216342.62662035,
       170437.84645866, 161867.77079652, 172931.18193329, 179518.99750296,
       170633.94263419, 231758.74957237, 165688.44346636, 196814.43598111,
       182138.71620403, 153274.69216353, 193174.96272209, 170105.27725175,
       166301.77817241, 168042.30117383, 313422.44752267, 173297.28936188,
       191709.23404109, 185429.26051895, 169048.26494759, 170182.40873714,
       186914.6433398 , 174398.94622224, 172901.54609802, 181708.41031374,
       239302.57036716, 182539.96149967, 207248.55201889, 203902.22162433,
       221258.4423809 , 266538.95762686, 175055.87375088, 183132.79166497,
       187103.47848795, 161085.83173088, 168803.2063704 , 174856.69519139,
       165642.24307103, 207138.20059692, 174319.03603115, 190499.19991999,
       207802.51256879, 190839.13429364, 178381.48787776, 185109.40727583,
       186918.41769757, 163276.2738914 , 184873.3662854 , 204594.36977033,
       185390.25316444, 167711.02219425, 178204.89873261, 199971.12805181,
       182830.10105994, 168482.15731611, 220839.08648636, 168485.05502618,
       172521.1058638 , 178330.97648738, 187607.85578113, 221649.80952747,
       186050.23007869, 180932.15881172, 183683.58786307, 318300.15326755,
       173949.16022791, 180189.07094983, 171072.05463687, 186377.11301301,
       181131.22424434, 192716.81580115, 188668.24789379, 181807.90424589,
       181979.06896399, 167950.81608211, 207295.12135992, 174707.1888586 ,
       162087.1069718 , 170353.04468486, 228586.10778558, 170387.80918474,
       217889.80349443, 196635.84067151, 186655.12150092, 211375.90937572,
       168589.6212344 , 180517.14153056, 178421.63166715, 169059.078348  ,
       177638.81242767, 200684.03404486, 177598.80581528, 165459.0787819 ,
       215787.94589257, 165726.00809738, 203752.42228058, 198894.08768408,
       250755.27150979, 180856.00054368, 194403.21440277, 173031.46919072,
       175615.18321219, 179504.76935724, 175454.84904986, 189866.31741414,
       354989.24870581, 192755.1822482 , 182661.73360415, 164750.03172125,
       194601.2635369 , 182143.46412985, 168704.78307375, 184916.66510001,
       173425.37332523, 193566.47686033, 177109.30986299, 263122.30265251,
       160518.95494465, 197544.40147966, 175098.03516933, 197011.17119831,
       186291.6569078 , 320116.82096716, 212065.99244981, 205582.63022041,
       163711.67531729, 164403.72850959, 179851.67005966, 173237.34934037,
       167120.54628801, 164852.10241945, 170889.54294184, 171493.60702028,
       168113.92514853, 181742.35196146, 183824.14814652, 181182.86719663,
       191927.32076433, 193462.9799785 , 187683.52326996, 205373.00319569,
       170261.11188124, 181013.17826521, 171446.82434309, 210021.90182207,
       173302.06131404, 169662.13297886, 169992.51720156, 177525.66328075,
       170373.24617923, 285135.51876301, 204443.43695345, 177764.87683796,
       212855.10462066, 166537.93911547, 199285.72593411, 196877.32319577,
       319520.94261658, 168363.74755756, 187596.54140719, 358213.79358847,
       189001.29547692, 165758.81327114, 168659.78170494, 161240.88387216,
       185795.63792101, 194549.39164684, 190557.58673723, 305173.38698387,
       232882.8815902 , 180801.18300371, 180836.39191137])
In [29]:
test_y
Out[29]:
723     124500.0
1445    179600.0
1447    157900.0
54      385000.0
1186    104900.0
          ...   
69       80000.0
1182    392500.0
1412    140000.0
312     132000.0
1450     92000.0
Name: SalePrice, Length: 439, dtype: float64
In [30]:
test_x
Out[30]:
0 1 2 3 4 5 6 7 8 9 ... 121 122 123 124 125 126 127 128 129 130
723 2.964688 1.108189 -1.526426 0.106318 -1.042796 -0.612592 -1.032391 1.350073 -1.213383 -0.725471 ... 0.254233 0.004565 0.166516 -0.192522 0.230416 -0.312816 -0.793391 0.295259 -0.816092 -0.227311
1445 2.735676 -1.591089 -1.468329 1.071727 0.432595 -1.166412 -0.803060 0.772676 1.314567 0.632887 ... 0.884464 -1.727236 -0.055649 0.800140 -0.018565 -0.294392 1.178796 -1.332034 -0.768121 -0.123243
1447 -5.013797 0.493032 -1.197866 -2.794624 -0.234594 -0.239627 1.083601 -0.860849 -0.511962 0.387550 ... -0.583836 -0.769786 0.890560 -0.117715 0.012387 -0.821743 0.472884 0.034400 -0.179110 0.043905
54 4.136926 -2.873939 0.595933 0.507417 2.241932 -0.854528 -2.331029 0.376094 -3.234142 -0.455991 ... 0.162953 -0.789771 0.236854 -0.045141 1.328041 1.117209 -0.023157 -0.171450 0.845562 0.614847
1186 6.723059 1.712578 5.890442 -1.582244 4.679489 4.702633 -1.451959 -0.309356 -0.251688 5.220498 ... 0.222234 -1.853359 0.033253 -0.361165 -1.636066 -0.768555 2.416819 0.819171 -0.792724 1.797923
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
69 2.172387 -0.808487 2.477093 -1.556855 -0.000057 0.542408 1.863973 -2.316841 -0.667263 -1.703224 ... -1.252408 0.645252 0.178384 -2.211365 -0.783801 0.160720 1.588978 -0.956403 0.773979 0.956843
1182 -6.409263 -2.428261 7.275868 -3.654681 -3.311630 11.733556 0.314062 1.212766 4.795634 -7.882574 ... 0.166486 0.592590 -0.657239 0.346196 0.694975 -0.667955 0.060696 1.738588 -1.676723 0.975703
1412 6.733677 3.457037 1.266721 2.745734 2.062651 0.352802 11.022980 -3.396504 -3.744251 -3.583932 ... 0.597551 -1.127908 -1.421101 -0.401232 1.240268 -1.021330 0.972074 0.034435 -0.147382 -0.074097
312 4.952066 1.422160 -1.451887 0.550482 2.051023 2.498262 -3.053314 1.050818 -0.896263 1.200291 ... 1.465287 -0.389000 -1.401231 0.186633 -0.178377 0.620381 -1.325567 0.570278 0.470974 0.453561
1450 1.621479 0.129355 -3.111755 -0.526160 1.554192 0.909834 3.317888 -0.116084 -2.044209 -0.761061 ... 0.172002 0.967188 0.243369 0.678146 0.880746 -0.424442 1.744801 -0.563968 0.833185 0.814238

439 rows × 131 columns

In [31]:
#MSE
print(mean_squared_error(test_y, predict1)**0.5) #rmse

81553.2328192636

3.2 model2 - GBM

In [29]:
from sklearn.ensemble import GradientBoostingRegressor

gb_clf = GradientBoostingRegressor(random_state = 0)
gb_clf.fit(train_x, train_y) 
이때, predict _ 
gb_pred = gb_clf.predict(test_x)
print(mean_squared_error(test_y, gb_pred)**0.5) #rmse

86231.12024571189
In [30]:
gb_pred
Out[30]:
array([180090.65878652, 180194.62118792, 178373.95764372, 174822.92370776,
       138934.49610489, 156865.55954029, 164799.28854393, 216436.88077777,
       161360.16703368, 188634.53687392, 145561.51441596, 193454.10876369,
       162049.4291099 , 160753.10472021, 185361.06183909, 191406.52071744,
       188461.05617389, 197192.09394978, 149926.54875668, 158640.26829547,
       134398.96691226, 153139.37832544, 154482.60219034, 152701.34162143,
       181026.85056599, 181849.69480777, 194203.14568695, 156994.75917599,
       214544.44594557, 331492.16154611, 189529.18202521, 165373.42754726,
       165559.63547916, 186053.04872021, 214893.7881934 , 209859.16368319,
       167310.88657884, 207763.27877076, 201263.59606257, 142392.74571533,
       211908.4787579 , 183238.28355833, 214708.87689714, 179359.30084616,
       169189.48363774, 153269.76395589, 151519.13997429, 179178.99701203,
       171922.60865982, 188594.26989367, 180074.96931598, 208650.17634869,
       204081.2762824 , 163818.30866441, 277864.93490689, 198543.43598868,
       225925.38037279, 223729.74368159, 184114.84665957, 222400.30356906,
       196427.82056872, 204648.55431559, 174471.96258975, 155496.47777149,
       168145.62504826, 193096.0920594 , 161044.45842093, 202844.23649115,
       199345.645978  , 156136.74734213, 171124.55213199, 221440.12834889,
       198696.33036989, 200260.17478093, 164566.27284388, 183834.68149496,
       186156.71276478, 170002.83860072, 190018.10166141, 189141.79959738,
       165499.70953747, 348607.86994486, 224850.12806853, 149573.03342503,
       160521.03221253, 157826.75432039, 204960.67243326, 150870.25460843,
       246017.4291673 , 207966.59211126, 190269.65310346, 178247.76473937,
       207329.04578477, 180344.15533868, 203551.28687516, 205895.67827976,
       201976.14005032, 169552.20847316, 157865.68320434, 188741.92838732,
       180763.84184319, 184411.32554114, 203777.47282951, 170871.91285812,
       203771.75756006, 195329.04775483, 165543.83634719, 165838.31061542,
       199552.90639895, 151453.8628795 , 201006.20276964, 150336.10388628,
       237010.21475533, 167398.00531837, 172764.89699673, 160195.70988638,
       130137.5047433 , 173998.01446339, 174222.69737618, 199480.55385826,
       191696.81953069, 135580.02616792, 162972.51171592, 185264.01332346,
       243254.65345032, 189635.23623713, 153174.89136909, 189134.30996495,
       240313.22919278, 175102.58239862, 157019.39502983, 168347.33951357,
       210314.51168214, 176338.43545837, 202677.7362189 , 166293.75779692,
       178852.54409587, 218384.017527  , 179615.90514181, 399453.77904726,
       195569.17193582, 209398.5683525 , 166454.33042797, 167348.82828713,
       171803.42399124, 235705.28685413, 243550.37322434, 175732.3781936 ,
       171843.82887081, 213641.26432618, 142969.50936745, 161229.66933407,
       148426.27266202, 174729.06705973, 191781.46036723, 156149.41380651,
       208122.12433912, 169535.44982483, 164481.08437487, 202575.00130472,
       206652.82765958, 205695.06247495, 217357.1287975 , 228695.93940945,
       197560.24739161, 198096.12733597, 146473.47377688, 175324.20833002,
       192222.96437434, 180069.79117816, 167531.8745685 , 198738.18337164,
       137327.97562239, 195908.09155421, 207710.2708911 , 164302.33334426,
       196095.87882227, 194616.07771942, 155825.50729963, 175020.46344822,
       174392.85847973, 174879.14863964, 176413.26549539, 189002.70700499,
       138445.06673538, 188622.54315204, 301332.93119747, 164073.88653556,
       202991.25611312, 149262.15575457, 155818.75535889, 208858.95111115,
       175923.59389918, 150158.98485147, 213754.02345278, 162412.69951067,
       287970.318786  , 263494.27918797, 167261.7862372 , 132885.18181943,
       187331.95700442, 217867.91813841, 145927.38177187, 181374.17906613,
       154044.9663035 , 185804.06236336, 160346.85115057, 151772.16048316,
       185123.92215738, 175976.99942653, 162223.1847279 , 183724.12624682,
       150239.8483688 , 200528.21537356, 268389.25244948, 172578.81503849,
       195193.10535513, 178955.35972482, 148413.84668668, 168062.3062221 ,
       182045.94235937, 187490.75214669, 178297.41791104, 162477.19744704,
       190174.0093809 , 198004.60118966, 193659.38270042, 189506.83887808,
       175951.8163147 , 365863.55137208, 186926.42299268, 171577.20323059,
       241535.21494513, 235510.26683741, 219332.59655335, 370439.38692558,
       193338.93428857, 173761.25818885, 189317.82921669, 190167.0741003 ,
       162370.44893871, 210697.58344289, 184771.74455743, 175893.1443015 ,
       186973.82218772, 177621.63081877, 180452.20470334, 208947.20955138,
       155464.16346715, 167734.15964443, 238619.16070939, 139945.79149113,
       148723.11598262, 198177.95241293, 258003.20709271, 187109.76591551,
       183737.69718636, 223180.09703792, 186049.42161655, 189431.26290738,
       148830.05229918, 166903.975006  , 179986.81441872, 160756.17762163,
       160633.59134204, 380054.40988535, 177101.39800785, 164944.05372734,
       174176.84532396, 180970.84459324, 124343.75003986, 187602.48070445,
       171277.3556903 , 190714.27148366, 177442.92728434, 175935.9044163 ,
       169566.18744725, 157040.00342309, 227960.14639574, 169820.57716441,
       475153.49908209, 175325.97452401, 164177.45910618, 183035.00176936,
       197773.29508681, 165158.62920466, 157243.08555486, 230150.92699678,
       214010.2040523 , 162207.35437734, 207025.41334213, 188639.61474974,
       159048.77940768, 211981.35143597, 168497.21385684, 186548.43449385,
       273575.37670184, 171295.64457453, 584255.25344569, 196612.52109597,
       199538.84322144, 169211.26380152, 227415.9878593 , 197418.44872096,
       182079.49605089, 157419.96949123, 188433.26820045, 159317.99196033,
       161822.83844396, 218111.73885991, 161558.41853262, 194351.33250217,
       166551.7276967 , 167198.47685993, 182087.3707697 , 199550.94486226,
       211277.98497117, 194855.84773148, 194015.18661373, 154642.42651508,
       180719.00542163, 171823.20121591, 181911.89103388, 197569.05752168,
       207379.19381217, 191009.78300244, 157432.04031991, 181233.20735627,
       190960.24047956, 257711.39561435, 158170.56234387, 232631.29958632,
       168870.55415334, 236343.68998325, 174388.02131455, 230894.2167905 ,
       179530.1613262 , 147145.63070697, 188453.71367726, 204517.56004665,
       135108.33424556, 188802.87610497, 163824.37096541, 188597.72630333,
       201815.56626537, 187945.49950822, 171727.69005749, 178696.11973444,
       212464.65122835, 187441.54423546, 168012.20720875, 204920.325722  ,
       171689.31309375, 161805.1785489 , 186273.86616676, 178607.25364404,
       202810.90239123, 204797.56733911, 192054.57390997, 216407.17084078,
       180937.02826789, 183150.91425419, 204457.79017607, 232671.88672745,
       191555.95552037, 211153.51559799, 151839.52446895, 176094.04151007,
       152376.57757492, 180152.36663611, 152629.3698433 , 166681.17548773,
       160935.13850895, 206864.81812145, 164977.63086432, 197039.95411956,
       152080.13974498, 191881.53799129, 227703.14118701, 153786.68170544,
       141435.25022524, 193162.42710413, 204360.00391154, 405194.35962149,
       171367.34094716, 173900.3783018 , 163574.6708386 , 140210.6381644 ,
       190877.76591788, 227644.76219253, 172171.26938983, 230515.47964311,
       177235.96550943, 169326.52373092, 155525.47677134, 178905.46333537,
       244606.64441916, 159013.02002545, 154806.87558884, 171281.45492232,
       151861.65127243, 212994.39938345, 195996.67178628, 182499.77481231,
       172645.45686385, 161666.21404551, 234020.50898558, 208507.63359797,
       171456.98261248, 219802.04522375, 164929.13905667, 142459.50275551,
       197275.75865999, 167218.35883903, 255311.96768829, 184315.72472937,
       191551.34538892, 209426.81604064, 182099.88627589, 200235.83375452,
       169961.8218497 , 184797.22904891, 168959.70861935, 207304.01309823,
       184470.42811695, 168907.05887625, 171798.6773019 , 191471.34404045,
       198784.8460898 , 259636.11356264, 167363.12527522, 183820.62962127,
       189339.90689467, 143069.35025514, 189936.66731099, 202527.16600682,
       200288.61220431])
In [31]:
test_y
Out[31]:
703     361919.0
668     193500.0
770     538000.0
528     446261.0
241     262500.0
          ...   
587     130000.0
73      185000.0
775     395000.0
1336    167900.0
1164    108959.0
Name: SalePrice, Length: 437, dtype: float64

3.3 model2 - xgboost

In [33]:
#!pip install xgboost
import xgboost

Collecting xgboost
  Downloading xgboost-1.6.2-py3-none-win_amd64.whl (125.4 MB)
Requirement already satisfied: numpy in c:\users\kim\anaconda3\lib\site-packages (from xgboost) (1.18.1)
Requirement already satisfied: scipy in c:\users\kim\anaconda3\lib\site-packages (from xgboost) (1.4.1)
Installing collected packages: xgboost
Successfully installed xgboost-1.6.2
In [232]:
xgb_model = xgboost.XGBRegressor(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,
                           colsample_bytree=1, max_depth=7)
xgb_model.fit(train_x,train_y)
xgb_pred = xgb_model.predict(test_x)
In [242]:
print(mean_squared_error(test_y, xgb_pred)**0.5) #rmse

86369.76066563524

3.3 model3 - light gbm

LightGBM is fast and accurate model, but sometimes occurs overfitting problems when data size is small.
In [34]:
from lightgbm import LGBMRegressor

lgb_clf = LGBMRegressor(num_leaves=31, objective='binary')
lgb_clf.fit(train_x, train_y)
lgbm_pred = lgb_clf.predict(test_x)
In [35]:
print(mean_squared_error(test_y, lgbm_pred)**0.5) #rmse

195432.12411843642

3.4 model 4 - ANN(3-layers)

In [25]:
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultOutRegressor(nn.Module):
    def __init__(self, input_dim, target_dim, hidden_dim=32,seed=1234):
        super().__init__()
        torch.manual_seed(seed)
        self.hidden_dim = hidden_dim
        self.target_dim = target_dim

        self.fc1 = nn.Linear(input_dim, self.hidden_dim)
        self.fc2 = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.fc3 = nn.Linear(self.hidden_dim, self.target_dim)

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.fc3(x)
        return x
In [26]:
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class TabularDataSet(Dataset) :
    def __init__(self, X , Y) :
        self._X = np.float32(X)
        self._Y = Y

    def __len__(self,) :
        return len(self._Y)

    def __getitem__(self,idx) :
        return self._X[idx], self._Y[idx]
In [27]:
from torch import optim
from IPython import display

#convert dataframe to array for DataLoader 
numpy_x = train_x.to_numpy()
numpy_y = train_y.to_numpy()

tabulardataset = TabularDataSet(numpy_x,numpy_y) 
train_dl = DataLoader(tabulardataset) 
model = MultOutRegressor(131 , 1)
optimizer = optim.AdamW(model.parameters(), lr=0.001) #default lr = 0.01
criterion = nn.MSELoss()
In [33]:
def update(input , target , model, criterion , optimizer,max_norm=5) :
    optimizer.zero_grad()
    output = model(input)
    print('output : ',output)
    loss = criterion(output , target.float())
    print('loss: ', loss)
    loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
    optimizer.step()
    return loss 

def one_epoch(dataloader , model, criterion , optimizer ) :
    result = torch.FloatTensor([0])
    print('epoch_result',result)
    for idx , (input , target) in enumerate(dataloader) :
        loss = update(input , target , model, criterion , optimizer)
        result = torch.add(result , loss)
    else :
        result /= idx+1
        return result.detach().cpu().numpy()

def visualize(result) :
    display.clear_output(wait=True)
    plt.plot(result)
    plt.show()

def train(n_epochs , dataloader , model, criterion , optimizer , log_interval=10) :
    epoch_loss = []
    for epoch in range(n_epochs) :
        loss = one_epoch(dataloader , model, criterion , optimizer )
        print('train_loss:',loss)
        if epoch > 0 :
            epoch_loss.append(loss)
        if epoch % log_interval == 0 :
            visualize(epoch_loss)
    else :
        return np.min(epoch_loss)
In [113]:
#train the model
train(500, train_dl ,model, criterion , optimizer,log_interval=50) #default n_epochs = 500 : min_loss = 2532927200.0

epoch_result tensor([0.])
output :  tensor([[157958.9688]], grad_fn=<AddmmBackward>)
loss:  tensor(292714.8125, grad_fn=<MseLossBackward>)
output :  tensor([[118984.3359]], grad_fn=<AddmmBackward>)
loss:  tensor(9094230., grad_fn=<MseLossBackward>)
output :  tensor([[195134.0625]], grad_fn=<AddmmBackward>)
loss:  tensor(870605.6250, grad_fn=<MseLossBackward>)
output :  tensor([[175495.2031]], grad_fn=<AddmmBackward>)
loss:  tensor(2264413.7500, grad_fn=<MseLossBackward>)
output :  tensor([[155584.6250]], grad_fn=<AddmmBackward>)
loss:  tensor(5.0245e+08, grad_fn=<MseLossBackward>)
output :  tensor([[127889.6797]], grad_fn=<AddmmBackward>)
loss:  tensor(12170.5713, grad_fn=<MseLossBackward>)
output :  tensor([[141483.0156]], grad_fn=<AddmmBackward>)
loss:  tensor(233304.0938, grad_fn=<MseLossBackward>)
output :  tensor([[119956.7031]], grad_fn=<AddmmBackward>)
loss:  tensor(915280.8750, grad_fn=<MseLossBackward>)
output :  tensor([[71885.3281]], grad_fn=<AddmmBackward>)
loss:  tensor(1.7917e+08, grad_fn=<MseLossBackward>)
output :  tensor([[165516.8906]], grad_fn=<AddmmBackward>)
loss:  tensor(233394.6719, grad_fn=<MseLossBackward>)
output :  tensor([[138299.5781]], grad_fn=<AddmmBackward>)
loss:  tensor(2891434.5000, grad_fn=<MseLossBackward>)
output :  tensor([[226129.7500]], grad_fn=<AddmmBackward>)
loss:  tensor(1276335., grad_fn=<MseLossBackward>)
output :  tensor([[169730.1250]], grad_fn=<AddmmBackward>)
loss:  tensor(533082.5000, grad_fn=<MseLossBackward>)
output :  tensor([[250104.6875]], grad_fn=<AddmmBackward>)
loss:  tensor(163771.9688, grad_fn=<MseLossBackward>)
output :  tensor([[55266.5117]], grad_fn=<AddmmBackward>)
loss:  tensor(71028.5000, grad_fn=<MseLossBackward>)
output :  tensor([[164872.0781]], grad_fn=<AddmmBackward>)
loss:  tensor(4.1502e+08, grad_fn=<MseLossBackward>)
output :  tensor([[167399.7812]], grad_fn=<AddmmBackward>)
loss:  tensor(57763324., grad_fn=<MseLossBackward>)
output :  
C:\Users\KIM\anaconda3\lib\site-packages\torch\nn\modules\loss.py:528: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  return F.mse_loss(input, target, reduction=self.reduction)

tensor([[162634.8906]], grad_fn=<AddmmBackward>)
loss:  tensor(37636884., grad_fn=<MseLossBackward>)
output :  tensor([[276178.3438]], grad_fn=<AddmmBackward>)
loss:  tensor(1388494., grad_fn=<MseLossBackward>)
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loss:  tensor(3064523.7500, grad_fn=<MseLossBackward>)
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loss:  tensor(2.2608e+09, grad_fn=<MseLossBackward>)
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loss:  tensor(5789663., grad_fn=<MseLossBackward>)
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loss:  tensor(2.7174e+08, grad_fn=<MseLossBackward>)
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loss:  tensor(1661722.3750, grad_fn=<MseLossBackward>)
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loss:  tensor(4992.3057, grad_fn=<MseLossBackward>)
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loss:  tensor(8889622., grad_fn=<MseLossBackward>)
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loss:  tensor(2.5473e+09, grad_fn=<MseLossBackward>)
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loss:  tensor(6004414., grad_fn=<MseLossBackward>)
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loss:  tensor(2.8901e+10, grad_fn=<MseLossBackward>)
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loss:  tensor(1.2912e+10, grad_fn=<MseLossBackward>)
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loss:  tensor(9081.4941, grad_fn=<MseLossBackward>)
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loss:  tensor(431351.3438, grad_fn=<MseLossBackward>)
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train_loss: [2.5573217e+09]
Out[113]:
2532927200.0
In [132]:
#test the model
ann_pred1 = model(torch.tensor(test_x.values).float())
print(mean_squared_error(test_y, ann_pred1.detach().numpy())**0.5) #rmse

99176.32901765831

3.5 model 5 - ANN(5-layers)

In [136]:
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultOutRegressor2(nn.Module):
    def __init__(self, input_dim, target_dim, hidden_dim=32,seed=1234):
        super().__init__()
        torch.manual_seed(seed)
        self.hidden_dim = hidden_dim
        self.target_dim = target_dim

        self.fc1 = nn.Linear(input_dim, self.hidden_dim)
        self.fc2 = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.fc3 = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.fc4 = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.fc5 = nn.Linear(self.hidden_dim, self.target_dim)

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.fc3(x)
        x = F.relu(x)
        x = self.fc4(x)
        x = F.relu(x)
        x = self.fc5(x)
        return x
In [137]:
from torch import optim
from IPython import display

#convert dataframe to array for DataLoader 
numpy_x = train_x.to_numpy()
numpy_y = train_y.to_numpy()

tabulardataset = TabularDataSet(numpy_x,numpy_y) 
train_dl = DataLoader(tabulardataset) 
model2 = MultOutRegressor2(131 , 1)
optimizer = optim.AdamW(model2.parameters(), lr=0.001) #default lr = 0.01
criterion = nn.MSELoss()
In [138]:
#train the model
train(500, train_dl ,model2, criterion , optimizer,log_interval=50) #default n_epochs = 500 : min_loss = 481092860

epoch_result tensor([0.])

C:\Users\KIM\anaconda3\lib\site-packages\torch\nn\modules\loss.py:528: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  return F.mse_loss(input, target, reduction=self.reduction)

train_loss: [4.9113827e+08]
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epoch_result tensor([0.])
train_loss: [4.782167e+08]
epoch_result tensor([0.])
train_loss: [4.6784163e+08]
epoch_result tensor([0.])
train_loss: [4.9556246e+08]
epoch_result tensor([0.])
train_loss: [4.748582e+08]
epoch_result tensor([0.])
train_loss: [4.8011414e+08]
epoch_result tensor([0.])
train_loss: [4.906535e+08]
epoch_result tensor([0.])
train_loss: [4.738452e+08]
epoch_result tensor([0.])
train_loss: [4.8260592e+08]
Out[138]:
444426100.0
In [139]:
#test the model
ann_pred2 = model2(torch.tensor(test_x.values).float())
print(mean_squared_error(test_y, ann_pred2.detach().numpy())**0.5) #rmse

147314.16941653125

cf) Tuning model's hyperparameters with K-fold cross validation

In [36]:
#training data for k-fold cross validation 
train_x_kf = pd.concat([train_x, valid_x], axis = 0)
train_y_kf = pd.concat([train_y, valid_y], axis = 0)

Random Forest - K-fold cross validation

In [39]:
from sklearn.model_selection import RandomizedSearchCV

#define rf_grid
# Number of trees in Random Forest
rf_n_estimators = [int(x) for x in np.linspace(200, 1000, 5)]
rf_n_estimators.append(1500)
rf_n_estimators.append(2000)

# Maximum number of levels in tree
rf_max_depth = [int(x) for x in np.linspace(5, 55, 11)]
# Add the default as a possible value
rf_max_depth.append(None)

# Number of features to consider at every split
rf_max_features = ['auto', 'sqrt', 'log2']

# Criterion to split on
#rf_criterion = ['mse', 'mae']

# Minimum number of samples required to split a node
rf_min_samples_split = [int(x) for x in np.linspace(2, 10, 9)]

# Minimum decrease in impurity required for split to happen
rf_min_impurity_decrease = [0.0, 0.05, 0.1]

# Method of selecting samples for training each tree
rf_bootstrap = [True, False]

rf_grid = {'n_estimators': rf_n_estimators,
               'max_depth': rf_max_depth,
               'max_features': rf_max_features,
               #'criterion': rf_criterion,
               'min_samples_split': rf_min_samples_split,
               'min_impurity_decrease': rf_min_impurity_decrease,
               'bootstrap': rf_bootstrap}

# Create the model to be tuned
rf_base = RandomForestRegressor()

# Create the random search Random Forest
rf_random = RandomizedSearchCV(estimator = rf_base, param_distributions = rf_grid, 
                               n_iter = 200, cv = 3, verbose = 2, random_state = 42, 
                               n_jobs = -1)

# Fit the random search model
rf_random.fit(train_x_kf, train_y_kf)

# View the best parameters from the random search
rf_random.best_params_

Fitting 3 folds for each of 200 candidates, totalling 600 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:  3.6min
[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed: 15.7min
[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed: 327.5min
[Parallel(n_jobs=-1)]: Done 600 out of 600 | elapsed: 369.1min finished
Out[39]:
{'n_estimators': 2000,
 'min_samples_split': 8,
 'min_impurity_decrease': 0.1,
 'max_features': 'log2',
 'max_depth': 5,
 'bootstrap': False}
In [40]:
#now try with random forest with "best" parameters
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import accuracy_score # 정확도 함수

clf_cv = RandomForestRegressor(n_estimators=200, max_depth=5,random_state=0,min_samples_split=8,min_impurity_decrease=0.1,max_features='log2',bootstrap=True)

clf_cv.fit(train_x,train_y)

predict1 = clf_cv.predict(test_x)
In [41]:
#MSE
from sklearn.metrics import mean_squared_error 
print(mean_squared_error(test_y, predict1)**0.5) #rmse

74292.61483889371

4. Ensemble with linear model at the end

https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/ (ensemble idea in this link is based on the k-fold cross validation to all base models)

- Each model's predict_y becomes the column of new train _X for linear model.

- Train_y(actual y values) becomes new train _Y for linear model

- Since we used 5 regression models, the final linear regression ensemble model has train _X of 5 columns and train_y of 1 column

In [ ]:
new_train_x = pd.concat([rf_pred_y, gbm_pred_y, xgboost_pred_y, ligbm_pred_y, ann_3_pred_y, ann_5_pred_y], axis=1)
new_train_y = train_y

new_test_x = test_x
new_test_y = test_y

ensemble_model = LogisticRegressor()

#train ensemble model with new_train_x and new_train_y
#test ensembel model with new_test_x and new_test_y